-
Hashim Sharif authoredHashim Sharif authored
tensorUtils.h 2.05 KiB
// Header guards
#ifndef UTILS_HEADER
#define UTILS_HEADER
#include "tensor.h"
#include <vector>
#include <stdlib.h>
std::vector<float> run_accuracies;
std::string model_params_path = "../../test/dnn_benchmarks/model_params/";
struct Tensor *readTrainedWeights(const char *file_name, int data_type,
long int dim1_size, long int dim2_size,
long int dim3_size, long int dim4_size) {
struct Tensor* weightsTensor = new struct Tensor;
return weightsTensor;
}
struct Tensor *readInputBatch(const char *file_name, long data_type,
long start, long end,
long dim2_size, long dim3_size, long dim4_size) {
struct Tensor* inputTensor = new struct Tensor;
return inputTensor;
}
uint8_t *readLabels(const char *labels_file, int num_labels) {
uint8_t* dummyLabels = (uint8_t*) malloc(sizeof(uint8_t) * num_labels);
return dummyLabels;
}
uint32_t *readLabels3(const char *labels_file, int num_labels) {
uint32_t* dummyLabels = (uint32_t*) malloc(sizeof(uint32_t) * num_labels);
return dummyLabels;
}
uint32_t *readLabelsBatch3(const char *labels_file, int start, int end) {
long int num_labels = end - start;
uint32_t* dummyLabels = (uint32_t*) malloc(sizeof(uint32_t) * num_labels);
return dummyLabels;
}
float computeAccuracy3(uint32_t *labels, void *result_ptr) {
return 100.0; // dummy return
}
// tensor_runtime.h empty definitions - for NVDLA-based compilation to work (functions not actually used)
void *create4DTensor(int data_type, int data_format, size_t dim1_size,
size_t dim2_size, size_t dim3_size, size_t dim4_size){
struct Tensor* weightsTensor = new struct Tensor;
return weightsTensor;
}
void startMemTracking(){
}
void freeBatchMemory(){
}
void hpvm_request_tensor(void *tensor, int destination){
}
void llvm_hpvm_initializeRuntimeController(const char *){
}
void llvm_hpvm_invokeRtControl(void *result,
const char *str,
int start,
int end){
}
#endif